{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5_wAO3emXGhe"
   },
   "outputs": [],
   "source": [
    "def show_images(imgs, num_rows, num_cols, scale=2):\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    for i in range(num_rows):\n",
    "        for j in range(num_cols):\n",
    "            axes[i][j].imshow(imgs[i * num_cols + j])\n",
    "            axes[i][j].axes.get_xaxis().set_visible(False)\n",
    "            axes[i][j].axes.get_yaxis().set_visible(False)\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VR8lr9WHEQWo"
   },
   "outputs": [],
   "source": [
    "# Install TensorFlow\n",
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "  pass\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RXOQ6qzaXKD_"
   },
   "source": [
    "# 9.9 语义分割和数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gPGR1YRsEjbL"
   },
   "source": [
    "在前几节讨论的目标检测问题中，我们一直使用方形边界框来标注和预测图像中的目标。本节将探讨语义分割（semantic segmentation）问题，它关注如何将图像分割成属于不同语义类别的区域。值得一提的是，这些语义区域的标注和预测都是像素级的。图9.10展示了语义分割中图像有关狗、猫和背景的标签。可以看到，与目标检测相比，语义分割标注的像素级的边框显然更加精细。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Gyt1z6DF5Q4"
   },
   "source": [
    "![alt text](http://tangshusen.me/Dive-into-DL-PyTorch/img/chapter09/9.9_segmentation.svg)\n",
    "\n",
    "图9.10 语义分割中图像有关狗、猫和背景的标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aoXfJZwIF9zp"
   },
   "source": [
    "## 9.9.1 图像分割和实例分割"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SVcaWb19GMSl"
   },
   "source": [
    "计算机视觉领域还有2个与语义分割相似的重要问题，即图像分割（image segmentation）和实例分割（instance segmentation）。我们在这里将它们与语义分割简单区分一下。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3nxQiHLSGbls"
   },
   "source": [
    "* 图像分割将图像分割成若干组成区域。这类问题的方法通常利用图像中像素之间的相关性。它在训练时不需要有关图像像素的标签信息，在预测时也无法保证分割出的区域具有我们希望得到的语义。以图9.10的图像为输入，图像分割可能将狗分割成两个区域：一个覆盖以黑色为主的嘴巴和眼睛，而另一个覆盖以黄色为主的其余部分身体。\n",
    "* 实例分割又叫同时检测并分割（simultaneous detection and segmentation）。它研究如何识别图像中各个目标实例的像素级区域。与语义分割有所不同，实例分割不仅需要区分语义，还要区分不同的目标实例。如果图像中有两只狗，实例分割需要区分像素属于这两只狗中的哪一只。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mTwNjUjgG7LG"
   },
   "source": [
    "## 9.9.2 Pascal VOC2012语义分割数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CapnjjmNG8OA"
   },
   "source": [
    "语义分割的一个重要数据集叫作Pascal VOC2012 [1]。为了更好地了解这个数据集，我们先导入实验所需的包或模块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OJh8F9fcEZDN"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "from tqdm import tqdm\n",
    "import sys\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0-hyqASnHYk-"
   },
   "source": [
    "我们先下载这个数据集的压缩包（[下载地址](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)）。压缩包大小是2 GB左右，下载需要一定时间。下载后解压得到VOCdevkit/VOC2012文件夹，然后将其放置在data文件夹下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ICIzu0JpHW67",
    "outputId": "1bd21164-eef1-4d9a-c48c-5a09818f492a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 115,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 下载并解压文件到data目录\n",
    "import requests\n",
    "import tarfile\n",
    "\n",
    "r = requests.get(\"http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar\")\n",
    "with open(\"data/VOCtrainval_11-May-2012.tar\", 'wb') as f:\n",
    "    f.write(r.content)\n",
    "\n",
    "def extract(tar_path, target_path):\n",
    "    try:\n",
    "        t = tarfile.open(tar_path)\n",
    "        t.extractall(path = target_path)\n",
    "        return True\n",
    "    except:\n",
    "        return False\n",
    "\n",
    "extract(\"data/VOCtrainval_11-May-2012.tar\", \"data/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wWjTFETRMtoi"
   },
   "source": [
    "解压后包含：\n",
    "\n",
    "![image.png]()\n",
    "\n",
    "```\n",
    "Annotations        JPEGImages         SegmentationObject\n",
    "ImageSets          SegmentationClass\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5x8shqXUNB8P"
   },
   "source": [
    "进入data/VOCdevkit/VOC2012路径后，我们可以获取数据集的不同组成部分。其中ImageSets/Segmentation路径包含了指定训练和测试样本的文本文件，而JPEGImages和SegmentationClass路径下分别包含了样本的输入图像和标签。这里的标签也是图像格式，其尺寸和它所标注的输入图像的尺寸相同。标签中颜色相同的像素属于同一个语义类别。下面定义read_voc_images函数将输入图像和标签读进内存。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VenXeQKQP9R9"
   },
   "source": [
    "train.txt文件内容如下：\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "2007_000032\n",
    "2007_000039\n",
    "2007_000063\n",
    "2007_000068\n",
    "2007_000121\n",
    "2007_000170\n",
    "...\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JeU-vU0uMmBA"
   },
   "outputs": [],
   "source": [
    "def read_voc_images(root=\"data/VOCdevkit/VOC2012\", \n",
    "            is_train=True, max_num=None):\n",
    "    txt_fname = '%s/ImageSets/Segmentation/%s' % (\n",
    "        root, 'train.txt' if is_train else 'val.txt')\n",
    "    with open(txt_fname, 'r') as f:\n",
    "        images = f.read().split()\n",
    "    if max_num is not None:\n",
    "        images = images[:min(max_num, len(images))]\n",
    "    features, labels = [None] * len(images), [None] * len(images)\n",
    "    for i, fname in tqdm(enumerate(images)):\n",
    "        feature_tmp = tf.io.read_file('%s/JPEGImages/%s.jpg' % (root, fname))\n",
    "        features[i] = tf.image.decode_jpeg(feature_tmp)\n",
    "        label_tmp = tf.io.read_file('%s/SegmentationClass/%s.png' % (root, fname))\n",
    "        labels[i] = tf.image.decode_png(label_tmp)\n",
    "    return features, labels #shape=(h, w, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "C98kOwFzSLFW",
    "outputId": "62cfe181-a955-4a72-f856-0a4723d926ac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 281)"
      ]
     },
     "execution_count": 117,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "root=\"data/VOCdevkit/VOC2012\"\n",
    "fname=\"2007_000032\"\n",
    "\n",
    "feature = Image.open('%s/JPEGImages/%s.jpg' % (root, fname)).convert(\"RGB\")\n",
    "label = Image.open('%s/SegmentationClass/%s.png' % (root, fname)).convert(\"RGB\")\n",
    "feature.size    #(w, h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "id": "HxLyodOoS_Qu",
    "outputId": "3aae6365-d401-4a33-ca01-7b863a44da25"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(281, 500, 3)\n",
      "(281, 500, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f482be6c400>"
      ]
     },
     "execution_count": 118,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fzcSVXXb/9Izte9g7fylxbZr7LXalqhtN7qpasNttDBt2K0OH3lIkiTscdgYM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\nVAjS5K6UUiFIk7tSSoUgTe5KKRWC/j9lTH1GE0TLzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = tf.io.read_file('%s/JPEGImages/%s.jpg' % (root, fname))\n",
    "feature = tf.image.decode_jpeg(feature)\n",
    "print(feature.shape)   #(h, w, c)\n",
    "label = tf.io.read_file('%s/SegmentationClass/%s.png' % (root, fname))\n",
    "label = tf.image.decode_png(label)\n",
    "print(label.shape)\n",
    "plt.imshow(label)   #(h, w, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "jDUZpTezTiJM",
    "outputId": "5b0f84c7-f32d-4ef9-b7e0-c56eb51a857b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:00, 212.86it/s]\n"
     ]
    }
   ],
   "source": [
    "voc_dir = \"data/VOCdevkit/VOC2012\"\n",
    "train_features, train_labels = read_voc_images(voc_dir, max_num=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "il65YbCQWO7B"
   },
   "source": [
    "我们画出前5张输入图像和它们的标签。在标签图像中，白色和黑色分别代表边框和背景，而其他不同的颜色则对应不同的类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "colab_type": "code",
    "id": "h_1KTxmyWBkl",
    "outputId": "ac37cc56-abf5-47e4-9dc5-dc0f714a3a8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f482db089e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482dacc208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482da7c438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482da2d668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d9dc898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f482d990ac8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d9c1cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d972f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d92d208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d8e23c8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 120,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ipGO+WlKPlcn2iCtXthCpMevx9ZhGle3xDk4d7XLJ/HRB2wVG3jN2FSkFZj4D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UFfIdApj8UCWgwAjSJNCnr/p7br2J1ukSYZRe7Y3t7i44/3ScuSS5evUDc1Vy/tMV9VrJZrfONw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\nnGhBosphcESJIqqU3yBDNGpGtobKSq7gjbPYRuJFLCLmFn2IwRgRhFor/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gAAIABJREFU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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "imgs = train_features[0:n] + train_labels[0:n]\n",
    "show_images(imgs, 2, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lSMlL3PtXoR9"
   },
   "source": [
    "接下来，我们列出标签中每个RGB颜色的值及其标注的类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dEJxCT6oXcQ3"
   },
   "outputs": [],
   "source": [
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128]]\n",
    "\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bs--H_hUa3VE"
   },
   "source": [
    "有了上面定义的两个常量以后，我们可以很容易地查找标签中每个像素的类别索引。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PKzTVCGVYBUm"
   },
   "outputs": [],
   "source": [
    "colormap2label = np.zeros(256 ** 3, dtype=np.uint8)\n",
    "for i, colormap in enumerate(VOC_COLORMAP):\n",
    "    colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n",
    "colormap2label = tf.convert_to_tensor(colormap2label)\n",
    "\n",
    "# 本函数已保存在d2lzh_pytorch中方便以后使用(没)\n",
    "def voc_label_indices(colormap, colormap2label):\n",
    "    \"\"\"\n",
    "    convert colormap (tf image) to colormap2label (uint8 tensor).\n",
    "    \"\"\"\n",
    "    colormap = tf.cast(colormap, dtype=tf.int32)\n",
    "    idx = tf.add(tf.multiply(colormap[:, :, 0], 256), colormap[:, :, 1])\n",
    "    idx = tf.add(tf.multiply(idx, 256), colormap[:, :, 2])\n",
    "    idx = tf.add(idx, colormap[:, :, 2])\n",
    "\n",
    "    # print(tf.gather_nd(colormap2label, tf.expand_dims(idx, -1)))\n",
    "    return tf.gather_nd(colormap2label, tf.expand_dims(idx, -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TRDG7CjCd0M9"
   },
   "source": [
    "例如，第一张样本图像中飞机头部区域的类别索引为1，而背景全是0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "tNW8GAETcPz7",
    "outputId": "07d6ede1-58ea-4725-87c9-1f317fcfbeb7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor: shape=(10, 10), dtype=uint8, numpy=\n",
       " array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 0, 1, 1, 1, 1],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]], dtype=uint8)>, 'aeroplane')"
      ]
     },
     "execution_count": 249,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = voc_label_indices(train_labels[0], colormap2label)\n",
    "y[105:115, 130:140], VOC_CLASSES[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TJvxftWWe_fZ"
   },
   "source": [
    "## 9.9.2.1 预处理数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5YIN2n6TfArl"
   },
   "source": [
    "在之前的章节中，我们通过缩放图像使其符合模型的输入形状。然而在语义分割里，这样做需要将预测的像素类别重新映射回原始尺寸的输入图像。这样的映射难以做到精确，尤其在不同语义的分割区域。为了避免这个问题，我们将图像裁剪成固定尺寸而不是缩放。具体来说，我们使用图像增广里的随机裁剪，并对输入图像和标签裁剪相同区域。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "23XIfJ7Se364"
   },
   "outputs": [],
   "source": [
    "def voc_rand_crop(feature, label, height, width):\n",
    "    \"\"\"\n",
    "    Random crop feature (tf image) and label (tf image).\n",
    "    先将channel合并，剪裁之后再分开\n",
    "    \"\"\"\n",
    "    combined = tf.concat([feature, label], axis=2)\n",
    "    last_label_dim = tf.shape(label)[-1]\n",
    "    last_feature_dim = tf.shape(feature)[-1]\n",
    "    combined_crop = tf.image.random_crop(combined,\n",
    "                        size=tf.concat([(height, width), [last_label_dim + last_feature_dim]],axis=0))\n",
    "    return combined_crop[:, :, :last_feature_dim], combined_crop[:, :, last_feature_dim:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 415
    },
    "colab_type": "code",
    "id": "D8yyAfLQmqaa",
    "outputId": "20fd00f9-e900-4819-cb8e-581c99e8887f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f482db49438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d9eaef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d8c0160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482e3dd8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482edb5d30>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f482d7cf208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d782438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d7366a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d6eb908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f482d69fac8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 127,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z+BUHoTNFCgaoxSEarbqxKiFGQtSvjC143RaIrNwFd3fusajnrJ4/oprf5vDw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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs = []\n",
    "for _ in range(n):\n",
    "    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)\n",
    "\n",
    "show_images(imgs[::2] + imgs[1::2], 2, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wrDcMtoAqjjv"
   },
   "source": [
    "## 9.9.2.2 自定义语义分割数据集类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QiNoccxhq1eN"
   },
   "source": [
    "我们通过自定义了一个获取语义分割数据集函数getVOCSegDataset。由于数据集中有些图像的尺寸可能小于随机裁剪所指定的输出尺寸，这些样本需要通过自定义的filter函数所移除。此外，我们还对输入图像的RGB三个通道的值分别做标准化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TGzlMKhXqwFg"
   },
   "outputs": [],
   "source": [
    "def getVOCSegDataset(is_train, crop_size, voc_dir, colormap2label, max_num=None):\n",
    "    \"\"\"\n",
    "    crop_size: (h, w)\n",
    "    \"\"\"\n",
    "    features, labels = read_voc_images(root=voc_dir, \n",
    "                        is_train=is_train,\n",
    "                        max_num=max_num)\n",
    "    def _filter(imgs, crop_size):\n",
    "        return [img for img in imgs if (\n",
    "            img.shape[0] >= crop_size[0] and\n",
    "            img.shape[1] >= crop_size[1])]\n",
    "    \n",
    "    def _crop(features, labels):\n",
    "        features_crop = []\n",
    "        labels_crop = []\n",
    "        for feature, label in zip(features, labels):\n",
    "            feature, label = voc_rand_crop(feature, label, \n",
    "                            height=crop_size[0],\n",
    "                            width=crop_size[1])\n",
    "            features_crop.append(feature)\n",
    "            labels_crop.append(label)\n",
    "        return features_crop, labels_crop\n",
    "    \n",
    "    def _normalize(feature, label):\n",
    "        rgb_mean = np.array([0.485, 0.456, 0.406])\n",
    "        rgb_std = np.array([0.229, 0.224, 0.225])\n",
    "        \n",
    "        label = voc_label_indices(label, colormap2label)\n",
    "        feature = tf.cast(feature, tf.float32)\n",
    "        feature = tf.divide(feature, 255.)\n",
    "        # feature = tf.divide(tf.subtract(feature, rgb_mean), rgb_std)\n",
    "        return feature, label\n",
    "\n",
    "    features = _filter(features, crop_size)\n",
    "    labels = _filter(labels, crop_size)\n",
    "    features, labels = _crop(features, labels)\n",
    "\n",
    "    print('read ' + str(len(features)) + ' valid examples')\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n",
    "    dataset = dataset.map(_normalize)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FSgNBbw-VTHI"
   },
   "source": [
    "## 9.9.2.3 读取数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pilZQWe2VVD9"
   },
   "source": [
    "我们通过自定义的getVOCSegDataset来分别创建训练集和测试集的实例。假设我们指定随机裁剪的输出图像的形状为320×400。下面我们可以查看训练集和测试集所保留的样本个数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "id": "0Ty3SZgdUcwi",
    "outputId": "e220808c-c887-4aed-b378-87b1a1d851e1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:00, 276.14it/s]\n",
      "29it [00:00, 281.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 77 valid examples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:00, 280.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 82 valid examples\n"
     ]
    }
   ],
   "source": [
    "crop_size = (320, 400)\n",
    "max_num = 100\n",
    "\n",
    "voc_train = getVOCSegDataset(True, crop_size, voc_dir, colormap2label, max_num)\n",
    "voc_test = getVOCSegDataset(False, crop_size, voc_dir, colormap2label, max_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hMltkHEBok7N"
   },
   "source": [
    "设批量大小为64，分别定义训练集和测试集的迭代器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IzKtBar4V4Ny"
   },
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "voc_train = voc_train.batch(batch_size)\n",
    "voc_test = voc_test.batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "r45P8h9boyEL",
    "outputId": "0b379766-29fe-4931-d845-24402a7495d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<dtype: 'float32'> (64, 320, 400, 3)\n",
      "<dtype: 'uint8'> (64, 320, 400)\n"
     ]
    }
   ],
   "source": [
    "for x, y in iter(voc_train):\n",
    "    print(x.dtype, x.shape)\n",
    "    print(y.dtype, y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AyTzRE0lpKUB"
   },
   "source": [
    "## 小结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0OrmGTjCpLna"
   },
   "source": [
    "* 语义分割关注如何将图像分割成属于不同语义类别的区域。\n",
    "* 语义分割的一个重要数据集叫作Pascal VOC2012。\n",
    "* 由于语义分割的输入图像和标签在像素上一一对应，所以将图像随机裁剪成固定尺寸而不是缩放。"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "“9.9 语义分割和数据集.ipynb”的副本",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
